In recent years, with the rapid development of deep learning techniques, a variety of image colorization models have been introduced and state-of-the-art performance on current datasets has been reported. Diverse deep-learning models ranging from the early brute-force networks (e.g.  [1]}) to the recently carefully designed Generative Adversarial Networks (GAN) (e.g.  [2]}) have been successfully used to tackle the colorization problem. These colorization networks differ in many major aspects, including but not limited to network architecture, loss types, learning strategies, network depth, etc.
